AI and Precision Medicine
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Sustainable Growth and Tech Trends
Publication | Update: Sep 2020
Precision medicine is a new advanced approach to facilitate quality patient care based on each patient’s genetic, lifestyle and environment.
Human diseases are complex with varying genetic, lifestyle and environmental factors. This means that the drug response should be different for patients even though they are suffering from the same disease. In this case, providing a right drug for each patient at the right time is more important. The right drug development strategy for each patient can only be fulfilled by precision medicine, which will play a key role in diagnosing the patient’s diseases faster and accurately.
It helps the doctors in providing quality treatment of the patient’s disease.
According to Varalaxmi Reddy of Proxzar, traditionally, the healthcare industry administers medication that could be common across several patients who are suffering with similar diseases.
With the advent of Artificial Intelligence, the healthcare industry has started an innovative idea to provide precision medicine – as opposed to similar medicine for all patients with similar ailments.
According to a recent study of globenewswire.com, the precision medicine market is expected to reach around USD 86.25 billion by 2025, growing at a CAGR of around 10.1% between 2019 and 2025.
The first step in providing personalized medicine is to identify biomarkers.
Biomarkers play an important role in developing precision medicine to diagnose diseases.
Biomarkers are playing a main role in identifying the right treatment process for the patients. Biomarker is a measurable indicator of patient disease stages. Doctors mostly depend upon the biomarkers to prepare the right medication treatment plan for the patients.
Biomarkers play multiple roles in curing the disease of patients by identifying the accurate stage of the disease as measured. They also encourage Doctors and Chemists to prepare precision medicine for the patients.
Biomarkers can be classified as:
Identify the disease as soon as possible. The diagnostic biomarker helps the doctors to confirm that the patients have particular health disorders.
Risk or Pre-Disposition biomarker
The risk stage of the patient while the disease is continuously increasing. The risk or pre-disposition biomarker helps the doctors to identify or suspect health disorders for particular patients in the near future.
Identifying the disease development. The prognostic biomarker helps the doctors to identify the possibility of recurrence of an already diagnosed disease. Take for example, breast cancer. In this case, the prognostic biomarker helps the doctors the most likely effect of breast cancer after taking initial treatment.
Identifying drug response from the patient. The predictive biomarkers can identify accurate treatment that is most likely to treat the patient and also provides information about how well the treatment is more likely to succeed for a particular patient.
Discovering suitable biomarkers to diagnose the stage of a certain disease involves substantial amount of manual work. The process of identifying the right biomarkers involves screening of tens and thousands of potential molecule candidates in patients. Artificial Intelligence algorithms could help in automating this manual discovery process.
AI and the role of biomarkers in precision medicine
To prepare medicines and diagnose the diseases, physicians must have to identify a statistical difference between the healthy and diseased human beings to mark various stages.
Traditionally, physicians manually evaluate every biomarker to find whether the identified stage is accurate or not, thereby spending great amount of time in manual evaluation. Also, patients have to wait for further medication.
The biomarkers are useful in clinical practice for:
- diagnosing and predicting the risk of patient’s diseases,
- identifying the signs of early-stage diseases from healthy human beings,
- deciding current treatment sufficient or not for the patients, and
- identifying specific target people who will help for a particular drug.
The clinical practice validation determines which biomarker is reliable, and provides an accurate measurement of disease stages.
There is huge amount of data available in the healthcare industry regarding biomarkers. Artificial intelligence can identify the potential biomarkers quickly and accurately to develop quality precision medicine. AI can collate this huge data and draw insights into which biomarker could provide accurate measurement.
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.